Imagine unlocking the full potential of large language models (LLMs) right on your local machine, without relying on costly cloud services. This is where Ollama shines by allowing users to harness the power of LLMs on their machines. While Ollama offers a range of ready-to-use models, there are times when a custom model is necessary, whether it’s fine-tuned on specific data or designed for a particular task. Efficiently deploying these custom models on local hardware often requires optimization techniques like quantization. In this article, we will explore the concept of quantization and demonstrate how to apply it to a fine-tuned model from Huggingface. We’ll then cover how to install Ollama, create a corresponding Modelfile for a custom model, and integrate this custom model into Ollama, proving how easy it is to bring AI capabilities in-house. All the code used in this article is available on Google Colab and in the LLM Tutorial.
Conclusion
This article has walked you through the process of quantizing a custom model, integrating it with Ollama, and testing it locally. By leveraging the llama.cpp framework we quantized our custom model in the Q4_K_M format and pushed it to Hugging Face Hub. We then discussed how to create the corresponding Modelfile and how to integrate our model into the Ollama framework.
Quantization, offers significant benefits, including reduced memory footprint, faster inference times, and lower power consumption. These advantages make it feasible to deploy sophisticated AI models across a variety of hardware configurations, from high-performance servers to low-power edge devices, broadening the scope of where and how AI can be applied. I hope you enjoyed reading this article and learned something new. You can find the quantized model from this example on Huggingface.
References
Brev.dev. (2024). Convert a fine-tuned model to GGUF format and run on Ollama. https://brev.dev/blog/convert-to-llamacpp
IBM. (2024). GGUF versus GGML. IBM. https://www.ibm.com/think/topics/gguf-versus-ggml
Ollama. (2024). Retrieved from https://ollama.com/blog
PatrickPT’s Blog. (2024). LLM Quantization in a nutshell. https://patrickpt.github.io/posts/quantllm/